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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def sigmod(x):\n",
    "    z = 1/(1+np.exp(-x))\n",
    "    return z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 参数初始化\n",
    "def initialize_params(dims):\n",
    "    w = np.zeros((dims, 1))\n",
    "    b = 0\n",
    "    return w, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logistic(X,y,w,b):\n",
    "    num_train = X.shape[0]      # 实例个数\n",
    "    num_feature = X.shape[1]    # 特征值个数\n",
    "\n",
    "    a = sigmod(np.dot(X, w) + b)   # 构建目标函数\n",
    "    # \n",
    "    cost = -1/num_train * np.sum(y*np.log(a) + (1-y)*np.log(1-a))\n",
    "\n",
    "    dw = np.dot(X.T, (a-y))/num_train\n",
    "    db = np.sum(a-y)/num_train\n",
    "    cost = np.squeeze(cost)\n",
    "    return a, cost, dw, db\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logistic_train(X, y, learning_rate, epochs):\n",
    "    # 初始化模型参数\n",
    "    w, b = initialize_params(X.shape[1])\n",
    "    cost_list = []\n",
    "\n",
    "    # 迭代训练\n",
    "    for i in range(epochs):\n",
    "        # 计算当前次的模型计算结果、损失和参数梯度\n",
    "        a, cost, dw, db = logistic(X,y,w,b)\n",
    "        # 参数更新\n",
    "        w = w-learning_rate * dw\n",
    "        b = b-learning_rate * db\n",
    "\n",
    "        # 记录损失\n",
    "        if i % 100 ==0:\n",
    "            cost_list.append(cost)\n",
    "        # 打印训练过程中的损失\n",
    "        if i % 100 == 0:\n",
    "            print(\"epoch %d cost %f\" %(i, cost))\n",
    "    # 保持参数\n",
    "    params ={\n",
    "        'w' :w,\n",
    "        'b' :b\n",
    "    }\n",
    "    # 保存梯度\n",
    "    grads = {\n",
    "        'dw' :dw,\n",
    "        'db' :db\n",
    "    }\n",
    "    return cost_list,params, grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 预测函数\n",
    "def predict(X,params):\n",
    "    y_prediction = sigmod(np.dot(X, params['w']) + params['b'])\n",
    "    for i in range(len(y_prediction)):\n",
    "        if y_prediction[i] >0.5:\n",
    "            y_prediction[i] = 1\n",
    "        else:\n",
    "            y_prediction[i] = 0\n",
    "    return y_prediction"
   ]
  },
  {
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   "execution_count": 11,
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# sjlearn生产模拟的二分类数据集\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets.samples_generator import make_classification\n",
    "X, labels = make_classification(n_samples=100, n_features=2,n_redundant=0,n_informative=2, random_state=1, n_clusters_per_class=2)\n",
    "print(labels)\n",
    "rng = np.random.RandomState(2)\n",
    "X+=2*rng.uniform(size=X.shape)\n",
    "\n",
    "unique_lables = set(labels)\n",
    "colors = plt.cm.Spectral(np.linspace(0,1,len(unique_lables)))\n",
    "for k,col in zip(unique_lables,colors):\n",
    "    x_k = X[labels==k]\n",
    "    plt.plot(x_k[:,0], x_k[:,1],'o',markerfacecolor=col,markeredgecolor='k',markersize=14)\n",
    "plt.title('data by make_classification')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(80,) (20,)\n(80, 1) (20, 1)\n"
    }
   ],
   "source": [
    "# 划分训练集和测试集\n",
    "offset = int(X.shape[0] * 0.8)\n",
    "X_train, y_train = X[:offset], labels[:offset]\n",
    "X_test, y_test = X[offset:], labels[offset:]\n",
    "print(y_train.shape, y_test.shape)\n",
    "y_train = y_train.reshape((-1,1))\n",
    "y_test = y_test.reshape((-1,1))\n",
    "print(y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "epoch 0 cost 0.693147\nepoch 100 cost 0.561989\nepoch 200 cost 0.489295\nepoch 300 cost 0.442785\nepoch 400 cost 0.410158\nepoch 500 cost 0.385813\nepoch 600 cost 0.366829\nepoch 700 cost 0.351531\nepoch 800 cost 0.338888\nepoch 900 cost 0.328230\nepoch 1000 cost 0.319099\nepoch 1100 cost 0.311174\nepoch 1200 cost 0.304220\nepoch 1300 cost 0.298062\nepoch 1400 cost 0.292567\nepoch 1500 cost 0.287629\nepoch 1600 cost 0.283166\nepoch 1700 cost 0.279110\nepoch 1800 cost 0.275409\nepoch 1900 cost 0.272017\nepoch 2000 cost 0.268896\nepoch 2100 cost 0.266016\nepoch 2200 cost 0.263349\nepoch 2300 cost 0.260874\nepoch 2400 cost 0.258569\nepoch 2500 cost 0.256420\nepoch 2600 cost 0.254410\nepoch 2700 cost 0.252527\nepoch 2800 cost 0.250760\nepoch 2900 cost 0.249099\nepoch 3000 cost 0.247534\nepoch 3100 cost 0.246058\nepoch 3200 cost 0.244664\nepoch 3300 cost 0.243346\nepoch 3400 cost 0.242097\nepoch 3500 cost 0.240913\nepoch 3600 cost 0.239789\nepoch 3700 cost 0.238721\nepoch 3800 cost 0.237705\nepoch 3900 cost 0.236737\nepoch 4000 cost 0.235815\nepoch 4100 cost 0.234935\nepoch 4200 cost 0.234095\nepoch 4300 cost 0.233292\nepoch 4400 cost 0.232524\nepoch 4500 cost 0.231789\nepoch 4600 cost 0.231085\nepoch 4700 cost 0.230411\nepoch 4800 cost 0.229764\nepoch 4900 cost 0.229143\nepoch 5000 cost 0.228547\nepoch 5100 cost 0.227974\nepoch 5200 cost 0.227423\nepoch 5300 cost 0.226894\nepoch 5400 cost 0.226384\nepoch 5500 cost 0.225893\nepoch 5600 cost 0.225420\nepoch 5700 cost 0.224964\nepoch 5800 cost 0.224525\nepoch 5900 cost 0.224100\nepoch 6000 cost 0.223691\nepoch 6100 cost 0.223296\nepoch 6200 cost 0.222914\nepoch 6300 cost 0.222545\nepoch 6400 cost 0.222189\nepoch 6500 cost 0.221844\nepoch 6600 cost 0.221510\nepoch 6700 cost 0.221187\nepoch 6800 cost 0.220875\nepoch 6900 cost 0.220572\nepoch 7000 cost 0.220279\nepoch 7100 cost 0.219995\nepoch 7200 cost 0.219719\nepoch 7300 cost 0.219452\nepoch 7400 cost 0.219193\nepoch 7500 cost 0.218941\nepoch 7600 cost 0.218698\nepoch 7700 cost 0.218461\nepoch 7800 cost 0.218231\nepoch 7900 cost 0.218008\nepoch 8000 cost 0.217791\nepoch 8100 cost 0.217580\nepoch 8200 cost 0.217375\nepoch 8300 cost 0.217176\nepoch 8400 cost 0.216982\nepoch 8500 cost 0.216794\nepoch 8600 cost 0.216611\nepoch 8700 cost 0.216432\nepoch 8800 cost 0.216259\nepoch 8900 cost 0.216090\nepoch 9000 cost 0.215925\nepoch 9100 cost 0.215765\nepoch 9200 cost 0.215609\nepoch 9300 cost 0.215457\nepoch 9400 cost 0.215309\nepoch 9500 cost 0.215165\nepoch 9600 cost 0.215024\nepoch 9700 cost 0.214887\nepoch 9800 cost 0.214753\nepoch 9900 cost 0.214623\n"
    }
   ],
   "source": [
    "cost_list, params, grads = logistic_train(X_train, y_train, 0.01, 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[1. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0. 1. 1. 0. 0. 1. 0.]]\n[[1 1 0 1 1 0 1 1 1 0 0 1 1 0 1 1 0 0 1 0]]\n"
    }
   ],
   "source": [
    "# 预测\n",
    "y_prediction = predict(X_test, params)\n",
    "print(y_prediction.T)\n",
    "print(y_test.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0.95\n"
    }
   ],
   "source": [
    "# 计算准确率\n",
    "def accuracy(y_test, y_pred):\n",
    "    correct_count = 0\n",
    "    for i in range(len(y_test)):\n",
    "        for j in range(len(y_pred)):\n",
    "            if y_test[i] == y_pred[j] and i == j:\n",
    "                correct_count += 1\n",
    "    accuracy_score = correct_count / len(y_test)\n",
    "    return accuracy_score\n",
    "accuracy_score_train = accuracy(y_test, y_prediction)\n",
    "print(accuracy_score_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}